Approximate reciprocal relationship between two cause-specific hazard ratios in COVID-19 data with mutually exclusive events

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2021-04-27 DOI:10.1101/2021.04.22.21255955

Wentian Li, S. Cetin, A. Ulgen, M. Cetin, Hakan Şıvgın, Yaning Yang

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引用次数: 3

Abstract

Abstract COVID-19 survival data presents a special situation where not only the time-to-event period is short, but also the two events or outcome types, death and release from hospital, are mutually exclusive, leading to two cause-specific hazard ratios (csHR d and csHR r ). The eventual mortality/release outcome is also analyzed by logistic regression to obtain odds-ratio (OR). We have the following three empirical observations: (1) The magnitude of OR is an upper limit of the csHR d : |log(OR)| ≥ |log(csHR d )|. This relationship between OR and HR might be understood from the definition of the two quantities; (2) csHR d and csHR r point in opposite directions: log(csHR d ) ⋅ log(csHR r ) < 0; This relation is a direct consequence of the nature of the two events; and (3) there is a tendency for a reciprocal relation between csHR d and csHR r : csHR d ∼ 1/csHR r . Though an approximate reciprocal trend between the two hazard ratios is in indication that the same factor causing faster death also lead to slow recovery by a similar mechanism, and vice versa, a quantitative relation between csHR d and csHR r in this context is not obvious. These results may help future analyses of data from COVID-19 or other similar diseases, in particular if the deceased patients are lacking, whereas surviving patients are abundant.

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在具有互斥事件的COVID-19数据中，两个病因特异性风险比之间存在近似的倒数关系

摘要新冠肺炎生存数据呈现出一种特殊情况，即不仅事件发生时间短，而且死亡和出院这两种事件或结果类型相互排斥，导致两种原因特异性风险比（csHR d和csHR r）。最终的死亡率/释放结果也通过逻辑回归进行分析，以获得比值比（OR）。我们有以下三个经验观察结果：（1）OR的大小是csHR d:|log（OR）|≥|log（csHR d）|的上限。OR和HR之间的这种关系可以从这两个量的定义中理解；（2） csHR d和csHR r指向相反的方向：log（csHR d）－log（csHRr）＜0；这种关系是这两个事件性质的直接结果；和（3）csHR d和csHR r之间存在一种相互关系的趋势：csHR d～1/csHR r。尽管两个危险比之间的近似倒数趋势表明，导致更快死亡的同一因素也会通过类似的机制导致缓慢恢复，反之亦然，但在这种情况下，csHR d和csHR r之间的定量关系并不明显。这些结果可能有助于未来分析新冠肺炎或其他类似疾病的数据，特别是如果死亡患者缺乏，而幸存患者充足。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Biostatistics Mathematics-Statistics and Probability

CiteScore

2.30

自引率

8.30%

发文量

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.